Visualization
library(ggridges)
library(tidyverse)## ── Attaching packages ─────────────────────────────────────────────────── tidyverse 1.2.1 ──## ✔ ggplot2 3.0.0 ✔ purrr 0.2.5
## ✔ tibble 1.4.2 ✔ dplyr 0.7.6
## ✔ tidyr 0.8.1 ✔ stringr 1.3.1
## ✔ readr 1.1.1 ✔ forcats 0.3.0## ── Conflicts ────────────────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ✖ ggplot2::scale_discrete_manual() masks ggridges::scale_discrete_manual()knitr::opts_chunk$set(
fig.width = 6,
fig.asp = .6,
out.width = "90%"
)weather_df =
rnoaa::meteo_pull_monitors(c("USW00094728", "USC00519397", "USS0023B17S"),
var = c("PRCP", "TMIN", "TMAX"),
date_min = "2017-01-01",
date_max = "2017-12-31") %>%
mutate(
name = recode(id, USW00094728 = "CentralPark_NY",
USC00519397 = "Waikiki_HA",
USS0023B17S = "Waterhole_WA"),
tmin = tmin / 10,
tmax = tmax / 10) %>%
select(name, id, everything())
weather_df## # A tibble: 1,095 x 6
## name id date prcp tmax tmin
## <chr> <chr> <date> <dbl> <dbl> <dbl>
## 1 CentralPark_NY USW00094728 2017-01-01 0 8.9 4.4
## 2 CentralPark_NY USW00094728 2017-01-02 53 5 2.8
## 3 CentralPark_NY USW00094728 2017-01-03 147 6.1 3.9
## 4 CentralPark_NY USW00094728 2017-01-04 0 11.1 1.1
## 5 CentralPark_NY USW00094728 2017-01-05 0 1.1 -2.7
## 6 CentralPark_NY USW00094728 2017-01-06 13 0.6 -3.8
## 7 CentralPark_NY USW00094728 2017-01-07 81 -3.2 -6.6
## 8 CentralPark_NY USW00094728 2017-01-08 0 -3.8 -8.8
## 9 CentralPark_NY USW00094728 2017-01-09 0 -4.9 -9.9
## 10 CentralPark_NY USW00094728 2017-01-10 0 7.8 -6
## # ... with 1,085 more rowsggplot(weather_df, aes(y=prcp, x=name)) + geom_boxplot()## Warning: Removed 3 rows containing non-finite values (stat_boxplot).
weather_df %>%
filter(prcp < 100) %>%
ggplot(aes(x = prcp, fill = name)) +geom_density(alpha = .5)
weather_df %>%
filter(prcp >0) %>%
ggplot(aes(x = prcp, fill = name)) +geom_density(alpha = .5)
Scatterplot
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5)## Warning: Removed 15 rows containing missing values (geom_point).
Formatting axes
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
)## Warning: Removed 15 rows containing missing values (geom_point).
Finer scaling of axes
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
scale_x_continuous(breaks = c(-15, 0, 15),
labels = c("-15º C", "0", "15"))## Warning: Removed 15 rows containing missing values (geom_point).
More manipulation sqrt does square root transformation
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
scale_x_continuous(breaks = c(-15, 0, 15),
labels = c("-15ºC", "0", "15"),
limits = c(-20, 30)) +
scale_y_continuous(trans = "sqrt",
position = "right")## Warning in self$trans$transform(x): NaNs produced## Warning: Transformation introduced infinite values in continuous y-axis## Warning: Removed 90 rows containing missing values (geom_point).
Color themes
Adjust color
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
scale_color_hue(
name = "Location",
h = c(100, 300),
l = 75)## Warning: Removed 15 rows containing missing values (geom_point).
Using the viridis package
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
)## Warning: Removed 15 rows containing missing values (geom_point).
Look up scale_color_viridis
Legend position
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme(legend.position = "bottom")## Warning: Removed 15 rows containing missing values (geom_point).
Theme bw - Changes background from gray to black and white
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme_bw() +
theme(legend.position = "bottom")## Warning: Removed 15 rows containing missing values (geom_point).
Classic theme
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme_classic() +
theme(legend.position = "bottom")## Warning: Removed 15 rows containing missing values (geom_point).
Minimal theme
ggplot(weather_df, aes(x = tmin, y = tmax)) +
geom_point(aes(color = name), alpha = .5) +
labs(
title = "Temperature plot",
x = "Minimum daily temperature (C)",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(
name = "Location",
discrete = TRUE
) +
theme_minimal() +
theme(legend.position = "bottom")## Warning: Removed 15 rows containing missing values (geom_point).
Always set theme_bw() before legend position theme
Always copy the following code shunk before making plots:
library(tidyverse)
knitr::opts_chunk$set( fig.width = 6, fig.asp = .6, out.width = “90%” )
theme_set(theme_bw() + theme(legend.position = “bottom”))
Really nice cLass example - For making plots
ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_smooth(se = FALSE) +
geom_point(aes(size = prcp), alpha = .75) +
labs(
title = "Temperature plot",
x = "Date",
y = "Maxiumum daily temperature (C)",
caption = "Data from the rnoaa package"
) +
viridis::scale_color_viridis(discrete = TRUE) +
theme_minimal() +
theme(legend.position = "bottom")## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).
Arguments for geom_
central_park =
weather_df %>%
filter(name == "CentralPark_NY")
waikiki =
weather_df %>%
filter(name == "Waikiki_HA")
ggplot(waikiki, aes(x = date, y = tmax, color = name)) +
geom_point() +
geom_line(data = central_park)## Warning: Removed 3 rows containing missing values (geom_point).
Patchwork
We’ve seen facetting as an approach to create the “same plot” for several levels of a categorical variable, and this can get you pretty far. Sometimes, though, you want to show two or three fundamentally different plots in the same graphic: you may want to juxtapose a scatterplot and a boxplot, or show scatterplots illustrating relationships between different variables. In this case, a solution is to create each of the panels you want separately and combine panels using tools in the patchwork package:
devtools::install_github("thomasp85/patchwork")## Skipping install of 'patchwork' from a github remote, the SHA1 (fd7958ba) has not changed since last install.
## Use `force = TRUE` to force installationLook into facetting that Jeff showed in class
library(patchwork)
tmax_tmin_p = ggplot(weather_df, aes(x = tmax, y = tmin, color = name)) +
geom_point(alpha = .5) +
theme(legend.position = "none")
prcp_dens_p = weather_df %>%
filter(prcp > 0) %>%
ggplot(aes(x = prcp, fill = name)) +
geom_density(alpha = .5) +
theme(legend.position = "none")
tmax_date_p = ggplot(weather_df, aes(x = date, y = tmax, color = name)) +
geom_point(alpha = .5) +
geom_smooth(se = FALSE) +
theme(legend.position = "bottom")
(tmax_tmin_p + prcp_dens_p) / tmax_date_p## Warning: Removed 15 rows containing missing values (geom_point).## `geom_smooth()` using method = 'loess' and formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).
Data manipulation
factors…
ggplot(weather_df, aes(x=name, y=tmax, fill=name)) +
geom_violin()## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
weather_df %>%
mutate(name = forcats::fct_relevel(name, c("Waikiki_HA", "CentralPark_NY", "Waterhole_WA"))) %>%
ggplot(aes(x = name, y = tmax)) +
geom_violin(aes(fill = name), color = "blue", alpha = .5) +
theme(legend.position = "bottom")## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
A second example reorders name according to tmax values in each name:
weather_df %>%
mutate(name = forcats::fct_reorder(name, tmax)) %>%
ggplot(aes(x = name, y = tmax)) +
geom_violin(aes(fill = name), color = "blue", alpha = .5) +
theme(legend.position = "bottom")## Warning: Removed 3 rows containing non-finite values (stat_ydensity).
Advanced tidying
weather_df %>%
select(name, tmax, tmin) %>%
gather(key = observation, value = temp, tmax:tmin) %>%
ggplot(aes(x = temp, fill = observation)) +
geom_density(alpha = .5) +
facet_grid(~name) +
viridis::scale_fill_viridis(discrete = TRUE)## Warning: Removed 18 rows containing non-finite values (stat_density).